Characteristics and risk factors for sibling incest
Bibliographic record
Abstract
Sibling sexual behaviour, despite historical and cross-cultural incest taboos and biologically driven incest avoidance, poses a persistent problem. We tested factors theorized to be associated with sibling incest in a cross-sectional online survey of 1,863 respondents with siblings mainly from North America and Germany. We found that 13% of participants reported engaging in sexual contact with a sibling, typically starting at the age of 10, and that step-siblings and half-siblings were more likely to engage in sibling incest than full siblings. Curiosity and games were the primary motivators; being coerced was more prevalent among female and younger participants. The study underscores both individual (e.g., impulsivity, concurrent childhood sexual behaviour problems) and family level factors (e.g., presence of step-sibling, positive attitudes toward nudity, sexual abuse by parent) influencing liability to engage in sexual behaviours with a sibling. Findings were robust across English- and German-speaking participants, suggesting our results are generalizable. Professionals addressing problematic child sexual behaviour should assess for concurrent sibling incest, and evaluate positive family attitudes towards nudity, sexual abuse by parents, and reduced disgust to sibling incest as potential risk factors for sibling incest. The findings stress the need for comprehensive sexual education in blended households, where age gaps and diminished genetic relatedness contribute to sibling sexual behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".